文章目录

• 一、Deep learning attracts lots of attention.
• • Ups and downs of Deep Learning
  • Three Steps for Deep Learning
  • Neural Network
  • Output Layer as Multi-Class Classifier
  • FAQ
• 二、Step 2: goodness of function
• • Loss for an Example：
  • Total Loss：
• 三、Step 3: pick the best function
• • Gradient Descent
  • Backpropagation
• 四、Backpropagation
• • Backpropagation – Forward pass
  • Backpropagation – Summary
• 总结

一、Deep learning attracts lots of attention.

Deep learning trends at Google. Source: SIGMOD 2016/Jeff Dean

Ups and downs of Deep Learning

1958: Perceptron (linear model)
1969: Perceptron has limitation
1980s: Multi-layer perceptron
Do not have significant difference from DNN today
1986: Backpropagation
Usually more than 3 hidden layers is not helpful
1989: 1 hidden layer is “good enough”, why deep?
2006: RBM initialization
2009: GPU
2011: Start to be popular in speech recognition
2012: win ILSVRC image competition
2015.2: Image recognition surpassing human-level performance
2016.3: Alpha GO beats Lee Sedol
2016.10: Speech recognition system as good as humans

Three Steps for Deep Learning

Neural Network

Network parameter θ: all the weights and biases in the “neurons” 。

Fully Connect Feedforward Network：




Deep = Many hidden layers：

Matrix Operation：

Neural Network ：

Output Layer as Multi-Class Classifier

Example Application–Handwriting Digit Recognition

ou need to decide the network structure to let a good function in your function set.

FAQ

Q: How many layers? How many neurons for each layer?

Q: Can the structure be automatically determined?
E.g. Evolutionary Artificial Neural Networks
Q: Can we design the network structure?

二、Step 2: goodness of function

Loss for an Example：

Total Loss：

Find a function in function set that minimizes total loss L.
Find the network parameters θ^∗ that minimize total loss L.

三、Step 3: pick the best function

Gradient Descent

Backpropagation

Backpropagation: an efficient way to compute ∂L∕∂w in neural network.

四、Backpropagation

Backpropagation – Forward pass

Compute ∂z∕∂w for all parameters

Backpropagation – Summary

总结